KVEraser enables efficient localized context erasing in large language models by replacing only the KV cache states of an erased span with learned steering states. It achieves near-full-recomputation performance on in-domain tasks across 1K to 32K context lengths, with only a 24% latency increase, and outperforms other approximate methods in long-document QA with 3--4x speedup over full recomputation.